Research And Implementation Of Parallel Lane Detection Algorithm Based On GPU

In the wake of the rapid development of society,more and more people have their own car in China.However,many troublesome questions are accompanying,such as traffic jams and traffic safety.The intelligent transportation system which aims to solve those traffic problems gets more attention now,and governments have invested a lot of resources to study it in the world.As a key part of intelligent transportation system,the intelligent vehicle is an intelligent platform which integrates multiple functions,such as environment perception,decision-making,and driver assistance,as the key component of environment perception system of intelligent vehicle,the lane detection technology based on computer vision has been the focus of research topics for scholars.The technology extracts location information of the lane in time through analyzing road images captured by vehicle-mounted cameras.The information is used in decision-making system,lane departure warning system,etc.Therefore,how to rapidly and accurately detect lane markings under complex road conditions is an import technology for intelligent vehicle.Because of high robustness in many complex road environment,the road detection algorithm based on texture feature and Hough transform,has become one of the methods to research lane detection.However,there are still some problems about a large amount of data,slow processing speed,high computational complexity and time-consuming in the traditional serial lane detection algorithm.Therefore,it is very difficult to meet the real-time requirements.Although some scholars have adopted a multi-core technology based on CPU to improve the speed of detecting line for Hough transform,the acceleration effect is not obvious.Graphic Processing Unit(GPU)with the powerful computing ability is widely used for parallel computing.According to the characteristics and advantages of GPU in data processing for general purpose computing,combined with the characteristics of image median filtering,differential excitation and Hough transform are suitable for parallelism,this paper proposed a parallel lane detection algorithm based on GPU acceleration.The proposed algorithm implements fast median filtering,differential excitation and Hough transform on compute unified device architecture(CUDA).This algorithm took the advantages of GPU in parallel computation,memory management and reasonably allocated the computational resources and the corresponding computational tasks to the host and device in the lane detection.Experimental results on an open database indicate that the proposed method outperforms the classical approaches,compared with common methods,the proposed method has some improvement in accuracy and speed.The main research content and contribution of this paper includes the following three aspects:Firstly,this paper proposed a special fast median filtering algorithm based on CUDA architecture.In the image preprocessing,after the region of interest in the original image is transformed into a grayscale image,the median filter is used to eliminate the influence of noise on grayscale.However,the median filter has a large amount of computation.In this paper,the characteristics of CUDA parallel programming model are used to optimize the original fast median filter,and parallel algorithm of fast median filter is implemented on CUDA platform.Compared with common methods,the proposed method has some improvement in speed.Then,a GPU-based parallelization method of differential excitation algorithm is proposed.After noise reduction,the differential excitation method is used to enhance the texture information in the gray image to extract the feature points of the lane line.Then according to Weber's theorem,we can selects the largest part of the information which can cause human visual system's attention,and the useful information to gain the binary image.According to the characteristics that differential excitation is suitable for parallel computing,we can use multi-threading method to process different pixels when calculate the neighborhood difference in differential excitation method,the proposed algorithm can effectively reduce the computational complexity of feature point extraction and improve the real-time performance of the algorithm by such parallel design.Finally,in view of shortages of large memory space and long computation time of the traditional Hough transform,a parallel Hough transform algorithm based on GPU is proposed in this paper.This algorithm designs the related parallel strategy based on the characteristics of the traditional Hough transform algorithm and implements it in the CUDA parallel programming model.In addition,we do some performance optimization for this algorithm,for example,calculating the part maximum value method instead of the traditional block seeking.The experimental results show that,compare to the typical methods,the proposed algorithm has better accuracy and less computation time.